System, Method, and Computer Program Product for Determining Long-Range Dependencies Using a Non-Local Graph Neural Network (GNN)

Abstract

Systems, methods, and computer program products for determining long-range dependencies using a non-local graph neural network (GNN): receive a dataset comprising historical data; generate at least one layer of a graph neural network by generating graph convolutions to compute node embeddings for a plurality of nodes of the dataset, the graph convolutions generated by aggregating node data from a first node of the dataset and node data from at least one second node comprising a neighbor node of the first node; cluster the node embeddings to form a plurality of centroids; determine an attention operator for at least one node-centroid pairing, the at least one node-centroid pairing comprising the first node and a first centroid; and generate relational data corresponding to a relation between the first node and at least one third node comprising a non-neighbor node of the first node using the attention operator.

What is claimed is: 
     
         1 . A method for determining long-range dependencies using a non-local graph neural network, comprising:
 receiving, with at least one processor, a dataset comprising historical data;   generating at least one layer of a graph neural network by generating, with at least one processor, graph convolutions to compute node embeddings for a plurality of nodes of the dataset, the graph convolutions generated by aggregating node data from a first node of the dataset and node data from at least one second node comprising a neighbor node of the first node;   clustering, with at least one processor, the node embeddings to form a plurality of centroids, each centroid corresponding to a graph-level representation of a plurality of node embeddings, the plurality of centroids comprising a first centroid;   determining, with at least one processor, an attention operator for at least one node-centroid pairing, the at least one node-centroid pairing comprising the first node and the first centroid, the attention operator configured to measure a similarity between the first node and the first centroid; and   generating, with at least one processor, relational data corresponding to a relation between the first node and at least one third node comprising a non-neighbor node of the first node using the attention operator.   
     
     
         2 . The method of  claim 1 , further comprising:
 generating, with at least one processor, a recommendation based on the relational data.   
     
     
         3 . The method of  claim 1 , wherein the first node corresponds to a first user, wherein the historical data comprises a plurality of first user-item pairings corresponding to historical transactions of the first user, wherein the method further comprises:
 generating, with at least one processor, a first recommendation for the first user based on the relational data, the first recommendation comprising an item not directly associated with the first user in the historical data; and   transmitting, with at least one processor, the first recommendation to a device of the first user.   
     
     
         4 . The method of  claim 1 , wherein a plurality of layers of the graph neural network are generated, wherein the clustering is performed in between each layer of the graph neural network generated, and each subsequent layer is generated using at least one centroid formed at a preceding layer. 
     
     
         5 . The method of  claim 1 , wherein the attention operator includes a multi-headed attention. 
     
     
         6 . The method of  claim 1 , further comprising:
 generating, with at least one processor, based on the attention operator and the aggregated node data from the first node and the at least one second node comprising the neighbor node of the first node, a mixed embedding, wherein the relational data is generated based on the mixed embedding.   
     
     
         7 . A system for determining long-range dependencies using a non-local graph neural network, comprising at least one processor programmed and/or configured to:
 receive a dataset comprising historical data;   generate at least one layer of a graph neural network by generating graph convolutions to compute node embeddings for a plurality of nodes of the dataset, the graph convolutions generated by aggregating node data from a first node of the dataset and node data from at least one second node comprising a neighbor node of the first node;   cluster the node embeddings to form a plurality of centroids, each centroid corresponding to a graph-level representation of a plurality of node embeddings, the plurality of centroids comprising a first centroid;   determine an attention operator for at least one node-centroid pairing, the at least one node-centroid pairing comprising the first node and the first centroid, the attention operator configured to measure a similarity between the first node and the first centroid; and   generate relational data corresponding to a relation between the first node and at least one third node comprising a non-neighbor node of the first node using the attention operator.   
     
     
         8 . The system of  claim 7 , wherein the at least one processor is programmed and/or configured to:
 generate a recommendation based on the relational data.   
     
     
         9 . The system of  claim 7 , wherein the first node corresponds to a first user, wherein the historical data comprises a plurality of first user-item pairings corresponding to historical transactions of the first user, wherein the at least one processor is programmed and/or configured to:
 generate a first recommendation for the first user based on the relational data, the first recommendation comprising an item not directly associated with the first user in the historical data; and   transmit the first recommendation to a device of the first user.   
     
     
         10 . The system of  claim 7 , wherein a plurality of layers of the graph neural network are generated, wherein the clustering is performed in between each layer of the graph neural network formed, and each subsequent layer is generated using at least one centroid generated at a preceding layer. 
     
     
         11 . The system of  claim 7 , wherein the attention operator includes a multi-headed attention. 
     
     
         12 . The system of  claim 7 , wherein the at least one processor is programmed and/or configured to:
 generate, based on the attention operator and the aggregated node data from the first node and the at least one second node comprising the neighbor node of the first node, a mixed embedding, wherein the relational data is generated based on the mixed embedding.   
     
     
         13 . A computer program product for determining long-range dependencies using a non-local graph neural network, comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:
 receive a dataset comprising historical data;   generate at least one layer of a graph neural network by generating graph convolutions to compute node embeddings for a plurality of nodes of the dataset, the graph convolutions generated by aggregating node data from a first node of the dataset and node data from at least one second node comprising a neighbor node of the first node;   cluster the node embeddings to form a plurality of centroids, each centroid corresponding to a graph-level representation of a plurality of node embeddings, the plurality of centroids comprising a first centroid;   determine an attention operator for at least one node-centroid pairing, the at least one node-centroid pairing comprising the first node and the first centroid, the attention operator configured to measure a similarity between the first node and the first centroid; and   generate relational data corresponding to a relation between the first node and at least one third node comprising a non-neighbor node of the first node using the attention operator.   
     
     
         14 . The computer program product of  claim 13 , wherein the program instructions cause the at least one processor to:
 generate a recommendation based on the relational data.   
     
     
         15 . The computer program product of  claim 13 , wherein the first node corresponds to a first user, wherein the historical data comprises a plurality of first user-item pairings corresponding to historical transactions of the first user, wherein the program instructions cause the at least one processor to:
 generate a first recommendation for the first user based on the relational data, the first recommendation comprising an item not directly associated with the first user in the historical data; and   transmit the first recommendation to a device of the first user.   
     
     
         16 . The computer program product of  claim 13 , wherein a plurality of layers of the graph neural network are generated, wherein the clustering is performed in between each layer of the graph neural network generated, and each subsequent layer is generated using at least one centroid formed at a preceding layer. 
     
     
         17 . The computer program product of  claim 13 , wherein the attention operator includes a multi-headed attention. 
     
     
         18 . The computer program product of  claim 13 , wherein the program instructions cause the at least one processor to:
 generate, based on the attention operator and the aggregated node data from the first node and the at least one second node comprising the neighbor node of the first node, a mixed embedding, wherein the relational data is generated based on the mixed embedding.